# Research Opportunities

## Postdoctoral Researchers

### Research Fellowship in Artificial Intelligence for Imaging

I have a vacancy for a Research Fellowship in Artificial Intelligence for Imaging to be hosted in the Department of Computer Science at UCL. The position is funded by Learned Exascale Computational Imaging (LEXCI) programme for the first two years with an option of subsequent employment as a staff scientist at UCL Advanced Research Computing (ARC) Centre.

The goal of LEXCI is to develop a new paradigm of exascale computational imaging, integrating hybrid model and data based approaches with uncertainty quantification at large scale and in distributed environments. During the project lifetime the team will focus on applications to imaging from observations of the next-generation of radio interferometric telescopes and imaging of neuronal pathways in the human brain through diffusion MRI.

LEXCI is driven by a multi-disciplinary team of experts in machine learning, statistics, applied mathematics, physics, high-performance computing, and software research engineering, led by Prof. Jason McEwen (UCL MSSL), Assoc. Prof. Marta Betcke (UCL CS), Rev. Dr Jeremy Yates (UCL CS), and Assoc. Prof. Marcelo Pereyra (Heriot-Watt University).

The successful candidate will be working with Assoc. Prof. Marta Betcke, Prof. Jason McEwen and Assoc. Prof. Marcelo Pereyra and will focus in particular on the development of statistical and numerical methods and algorithmic challenges in the context of big data and large scale, distributed hybrid architectures. They will also collaborate with Research Software Engineers in LEXCI team on deployment on modern and future high-performance computing infrastructure and with application domain specialists on knowledge transfer (astronomy, medical imaging and beyond).

This post is available to start as soon as possible but not later than September 2022 and is funded for 24 months in the first instance with the option of transitioning to an open-ended appointment as a staff scientist within UCL Advanced Research Computing (ARC) Centre at the end of this period, subject to satisfactory performance evaluation.

Further details and the application link (submission deadline 21 February 2022) are available here.

## Postdoctoral Fellowships

I very much encourage young researchers to apply for postdoctoral fellowships. I am happy to support and assist strong candidates that would like to apply for fellowships with MSSL as the host institution.

If you are interested in discussing this further then please email me, including '[Fellowship enquiry]' in the subject of your email. I receive many enquires and so will only reply if your expertise are well matched to my research interests and there is a high chance of submitting a successful application.

More information on various fellowships can he found here:

- Royal Society (RS) University Research Fellowship (URF)
- Royal Society (RS) Newton International Fellowships (for researchers coming from abroad)
- Royal Society (RS) Dorothy Hodgkin Fellowships
- STFC Ernest Rutherford Fellowships (ERF)
- Royal Astronomical Society (RAS) Fellowships
- Leverhulme Trust Early Career Fellowships (ECF)
- Royal Commission for the Exhibition of 1851 Fellowships
- Marie Curie Fellowships (for researchers coming from Europe)
- Daphne Jackson Fellowships (for researchers returning from a career break)

## PhD Students

The PhD projects that I offer are typically multi-disciplinary and include a combination of cosmology, statistics, and informatics (e.g. machine learning, signal processing, harmonic analysis, etc.). A relatively strong mathematical background is usually required for these types of projects. Strong programming skills are also an advantage.

If you are interested in discussing PhD projects further then please email me, including '[PhD enquiry]' in the subject of your email, and attach a CV. I receive many enquires and so will only reply if your expertise are well matched to my research interests and there is a high chance of submitting a successful application.

Further information on how to submit an official application to MSSL can be found here.

Further information on how to submit an official application via the UCL CDT in Data Intensive Science can be found here.

Brief overviews of current projects on offer are given below

### PhD project: Geometric deep learning on the celestial sphere for cosmology and beyond

The current evolution of our Universe is dominated by the influence of dark energy and dark matter, which constitute 95% of its content. However, an understanding of the fundamental physics underlying the dark Universe remains critically lacking. Forthcoming experiments have the potential to revolutionalise our understanding of the dark Universe. Both the ESA Euclid satellite and the Rubin Observatory Legacy Survey of Space and Time (LSST) will come online imminently, with Euclid scheduled for launch in 2022 and the Rubin LSST Observatory having recently achieved first light. Sensitive statistical and deep learning techniques are required to extract cosmological information from weak observational signatures of dark energy and dark matter.

Deep learning has been remarkably successful in the interpretation of standard (Euclidean) data, such as 1D time series data, 2D image data, and 3D video or volumetric data, now exceeding human accuracy in many cases. However, standard deep learning techniques fail catastrophically when applied to data defined on other domains, such as data defined over networks, 3D objects, or other manifolds such as the sphere. This has given rise to the field of geometric deep learning (Bronstein et al. 2017; Bronstein et al. 2021).

In cosmology, wide field observations are made on the celestial sphere giving rise to spherical 360° data, such as observations of the cosmic microwave background (CMB) relic radiation from the Big Bang and observations of cosmic shear of galaxies, which can be used to better understand the nature of dark matter and dark energy. Upcoming experiments such as Euclid and Rubin Observatory LSST will capture wide-field data for which the underlying spherical geometry must be accurately modelled. Thus, geometric deep learning techniques constructed natively on the sphere will be essential for next-generation deep learning analyses to extract cosmological information from these upcoming datasets.

McEwen and collaborators have recently developed efficient generalised spherical convolutional neutral networks (Cobb et al. 2021) and spherical scattering networks (McEwen et al. 2021) that have shown exceptional performance. These techniques are now starting to be applied in virtual reality and in medical imaging.

The focus of the current project is two-fold. First, further foundations of geometric deep learning on the sphere will be developed, including new types of spherical deep learning layers and architectures, in order to address the open problems in the field, such as scalability to large datasets and interpretability. Second, geometric deep learning techniques on the sphere will be applied to the analysis of cosmological data of the CMB and of cosmic shear, in particular from Euclid and the Rubin Observatory, in order to better understand the nature of dark matter and dark energy. Furthermore, additional applications beyond cosmology, such as for diffusion MRI in medical imaging, may also be considered. The precise focus between these different areas will depend on the interests and expertise of the student.

The student should have a strong mathematical background and be proficient in coding, particularly in Python. The student will gain extensive expertise during the project in deep learning, going far beyond the straightforward application of existing deep learning techniques, instead focusing on novel foundational deep learning approaches and their application to novel problems in cosmology and beyond. The expertise gained in foundational deep learning will prepare the student well for a future career either in academia or industry. In particular, geometric deep learning is a speciality highly sought after in industry by companies such as Twitter, Facebook, Amazon and many others, for the analysis of social networks and hierarchical data.

### PhD project: Geometric deep learning for likelihood-free statistical inference

Deep learning has been remarkably successful in the interpretation of standard (Euclidean) data, such as 1D time series data, 2D image data, and 3D video or volumetric data, now exceeding human accuracy in many cases. However, standard deep learning techniques fail catastrophically when applied to data defined on other domains, such as data defined over networks, graphs, 3D objects, or other manifolds such as the sphere. This has given rise to the field of geometric deep learning (Bronstein et al. 2017; Bronstein et al. 2021).

The bedrock of much scientific analysis is statistical inference, in particular Bayesian approaches. Recently, simulation-based inference techniques (cf. likelihood-free inference) have emerged, and are rapidly evolving, for scenarios where an explicit likelihood is not available or simply to speed up inference in time-critical applications (e.g. in gravitational wave detection for rapid electromagnetic follow-up). For a brief review see Cranmer et al. 2020. These techniques build on powerful machine learning models for probability distributions (e.g. Papamakarios et al. 2021).

The focus of the current project is to develop integrated geometric deep learning and simulation-based inference techniques (cf. likelihood-free inference) for data defined over complex domains, such as spherical manifolds and graphs. This will involve developing geometric emulation, imaging, and inference techniques as part of a overarching inference pipeline. A key component of such a pipeline will be geometric scattering network representations (Mallat 2012; McEwen et al. 2021). The techniques developed will have application in cosmology, medical imaging, geophysics and beyond; we will collaborate with others to apply them in the aforementioned fields.

The student should have a strong mathematical background and be proficient in coding, particularly in Python. Experience in deep learning is advantageous. The expertise gained in both geometric deep learning and likelihood-free inference will prepare the student well for a future research career either in academia or industry. In particular, both geometric deep learning and likelihood-free inference are specialities highly sought after in industry by companies such as Google, Twitter, Facebook, Amazon and many others.

## Masters Students

I am not offering any additional Masters projects at present but when I am they will appear here.

## Internship Students

I am not offering any specific internship projects at present. However, if you are interested in discussing internship possibilities further then please email me, including '[Internship enquiry]' in the subject of your email, and attach a CV. I receive many enquires and so will only reply if your expertise are well matched to my research interests and there is a high chance of a placement.